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Semantic Segmentation for Object Detection in Videos using Mask RCNN

Dharman J.

Abstract


The segmentation of object instances is an essential step in real-time video detection. Object detection is the problem of detecting the information of all types of items in an image by designating their location with a rectangular box. Using its remarkable feature-learning capabilities, deep learning promotes object detection research. Numerous researchers have utilised various machine learning algorithms for object detection and have focused on enhancing the accuracy of feature extraction. Due to this inaccuracy that occurred during the detection of the object at a lower level, neither the lower-level nor higher-level features are presented appropriately. FPN is utilised for feature extraction, which extracts both lower-level and higher-level features for reliable object recognition, and the proposed model takes advantage of these extracted characteristics handles the classification process (Mask R-CNN). This paper aims to develop a persuasive framework for instance divides.


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References


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